In Algorithm REF , we employ the importance sampling ratio to account for the discrepancy between the target policy \(\pi \)  and the behavior policy \(\pi _b\) .
Although all the three elements of the deadly triad (bootstrapping, function approximation, and off-policy sampling) [1]} are present, we show that by choosing \(n\)  appropriately,
Algorithm REF  has provable finite-sample convergence guarantee. The detailed statement of the result is presented in Section REF . In this section we provide some intuition.
